{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scipy: 0.18.1,s\n",
      "numpy: 1.11.1\n",
      "matplotlib: 1.5.3\n",
      "pandas: 0.18.1\n",
      "sklearn: 0.17.1\n",
      "statsmodels: 0.6.1\n"
     ]
    }
   ],
   "source": [
    "# scipy\n",
    "import scipy\n",
    "print('scipy: {},{}'.format(scipy.__version__,'s'))\n",
    "# numpy\n",
    "import numpy\n",
    "print('numpy: {}'.format(numpy.__version__))\n",
    "# matplotlib\n",
    "import matplotlib\n",
    "print('matplotlib: {}'.format(matplotlib.__version__))\n",
    "# pandas\n",
    "import pandas  \n",
    "print('pandas: {}'.format(pandas.__version__))\n",
    "# scikit-learn\n",
    "import sklearn\n",
    "print('sklearn: {}'.format(sklearn.__version__))\n",
    "# statsmodels\n",
    "import statsmodels\n",
    "print('statsmodels: {}'.format(statsmodels.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas  as  pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "series = pd.read_csv('robberies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series\n",
    "series = Series.from_csv('robberies.csv', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1966-01                                                                     41.0\n",
       "1966-02                                                                     39.0\n",
       "1966-03                                                                     50.0\n",
       "1966-04                                                                     40.0\n",
       "1966-05                                                                     43.0\n",
       "1966-06                                                                     38.0\n",
       "1966-07                                                                     44.0\n",
       "1966-08                                                                     35.0\n",
       "1966-09                                                                     39.0\n",
       "1966-10                                                                     35.0\n",
       "1966-11                                                                     29.0\n",
       "1966-12                                                                     49.0\n",
       "1967-01                                                                     50.0\n",
       "1967-02                                                                     59.0\n",
       "1967-03                                                                     63.0\n",
       "1967-04                                                                     32.0\n",
       "1967-05                                                                     39.0\n",
       "1967-06                                                                     47.0\n",
       "1967-07                                                                     53.0\n",
       "1967-08                                                                     60.0\n",
       "1967-09                                                                     57.0\n",
       "1967-10                                                                     52.0\n",
       "1967-11                                                                     70.0\n",
       "1967-12                                                                     90.0\n",
       "1968-01                                                                     74.0\n",
       "1968-02                                                                     62.0\n",
       "1968-03                                                                     55.0\n",
       "1968-04                                                                     84.0\n",
       "1968-05                                                                     94.0\n",
       "1968-06                                                                     70.0\n",
       "                                                                           ...  \n",
       "1973-06                                                                    279.0\n",
       "1973-07                                                                    309.0\n",
       "1973-08                                                                    401.0\n",
       "1973-09                                                                    309.0\n",
       "1973-10                                                                    328.0\n",
       "1973-11                                                                    353.0\n",
       "1973-12                                                                    354.0\n",
       "1974-01                                                                    327.0\n",
       "1974-02                                                                    324.0\n",
       "1974-03                                                                    285.0\n",
       "1974-04                                                                    243.0\n",
       "1974-05                                                                    241.0\n",
       "1974-06                                                                    287.0\n",
       "1974-07                                                                    355.0\n",
       "1974-08                                                                    460.0\n",
       "1974-09                                                                    364.0\n",
       "1974-10                                                                    487.0\n",
       "1974-11                                                                    452.0\n",
       "1974-12                                                                    391.0\n",
       "1975-01                                                                    500.0\n",
       "1975-02                                                                    451.0\n",
       "1975-03                                                                    375.0\n",
       "1975-04                                                                    372.0\n",
       "1975-05                                                                    302.0\n",
       "1975-06                                                                    316.0\n",
       "1975-07                                                                    398.0\n",
       "1975-08                                                                    394.0\n",
       "1975-09                                                                    431.0\n",
       "1975-10                                                                    431.0\n",
       "Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977)      NaN\n",
       "Name: Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977), dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "series=series[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1966-01     41.0\n",
       "1966-02     39.0\n",
       "1966-03     50.0\n",
       "1966-04     40.0\n",
       "1966-05     43.0\n",
       "1966-06     38.0\n",
       "1966-07     44.0\n",
       "1966-08     35.0\n",
       "1966-09     39.0\n",
       "1966-10     35.0\n",
       "1966-11     29.0\n",
       "1966-12     49.0\n",
       "1967-01     50.0\n",
       "1967-02     59.0\n",
       "1967-03     63.0\n",
       "1967-04     32.0\n",
       "1967-05     39.0\n",
       "1967-06     47.0\n",
       "1967-07     53.0\n",
       "1967-08     60.0\n",
       "1967-09     57.0\n",
       "1967-10     52.0\n",
       "1967-11     70.0\n",
       "1967-12     90.0\n",
       "1968-01     74.0\n",
       "1968-02     62.0\n",
       "1968-03     55.0\n",
       "1968-04     84.0\n",
       "1968-05     94.0\n",
       "1968-06     70.0\n",
       "           ...  \n",
       "1973-05    286.0\n",
       "1973-06    279.0\n",
       "1973-07    309.0\n",
       "1973-08    401.0\n",
       "1973-09    309.0\n",
       "1973-10    328.0\n",
       "1973-11    353.0\n",
       "1973-12    354.0\n",
       "1974-01    327.0\n",
       "1974-02    324.0\n",
       "1974-03    285.0\n",
       "1974-04    243.0\n",
       "1974-05    241.0\n",
       "1974-06    287.0\n",
       "1974-07    355.0\n",
       "1974-08    460.0\n",
       "1974-09    364.0\n",
       "1974-10    487.0\n",
       "1974-11    452.0\n",
       "1974-12    391.0\n",
       "1975-01    500.0\n",
       "1975-02    451.0\n",
       "1975-03    375.0\n",
       "1975-04    372.0\n",
       "1975-05    302.0\n",
       "1975-06    316.0\n",
       "1975-07    398.0\n",
       "1975-08    394.0\n",
       "1975-09    431.0\n",
       "1975-10    431.0\n",
       "Name: Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977), dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "split_point = len(series) - 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset,validation = series[:split_point] , series[split_point:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 106 , Validation 12\n"
     ]
    }
   ],
   "source": [
    "print('Dataset %d , Validation %d'%(len(dataset),len(validation)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "series=dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = series.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  41.,   39.,   50.,   40.,   43.,   38.,   44.,   35.,   39.,\n",
       "         35.,   29.,   49.,   50.,   59.,   63.,   32.,   39.,   47.,\n",
       "         53.,   60.,   57.,   52.,   70.,   90.,   74.,   62.,   55.,\n",
       "         84.,   94.,   70.,  108.,  139.,  120.,   97.,  126.,  149.,\n",
       "        158.,  124.,  140.,  109.,  114.,   77.,  120.,  133.,  110.,\n",
       "         92.,   97.,   78.,   99.,  107.,  112.,   90.,   98.,  125.,\n",
       "        155.,  190.,  236.,  189.,  174.,  178.,  136.,  161.,  171.,\n",
       "        149.,  184.,  155.,  276.,  224.,  213.,  279.,  268.,  287.,\n",
       "        238.,  213.,  257.,  293.,  212.,  246.,  353.,  339.,  308.,\n",
       "        247.,  257.,  322.,  298.,  273.,  312.,  249.,  286.,  279.,\n",
       "        309.,  401.,  309.,  328.,  353.,  354.,  327.,  324.,  285.,\n",
       "        243.,  241.,  287.,  355.,  460.,  364.,  487.])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  41.,   39.,   50.,   40.,   43.,   38.,   44.,   35.,   39.,\n",
       "         35.,   29.,   49.,   50.,   59.,   63.,   32.,   39.,   47.,\n",
       "         53.,   60.,   57.,   52.,   70.,   90.,   74.,   62.,   55.,\n",
       "         84.,   94.,   70.,  108.,  139.,  120.,   97.,  126.,  149.,\n",
       "        158.,  124.,  140.,  109.,  114.,   77.,  120.,  133.,  110.,\n",
       "         92.,   97.,   78.,   99.,  107.,  112.,   90.,   98.,  125.,\n",
       "        155.,  190.,  236.,  189.,  174.,  178.,  136.,  161.,  171.,\n",
       "        149.,  184.,  155.,  276.,  224.,  213.,  279.,  268.,  287.,\n",
       "        238.,  213.,  257.,  293.,  212.,  246.,  353.,  339.,  308.,\n",
       "        247.,  257.,  322.,  298.,  273.,  312.,  249.,  286.,  279.,\n",
       "        309.,  401.,  309.,  328.,  353.,  354.,  327.,  324.,  285.,\n",
       "        243.,  241.,  287.,  355.,  460.,  364.,  487.], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_size = int(len(X)*0.50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train,test = X[:train_size],X[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 125.,  155.,  190.,  236.,  189.,  174.,  178.,  136.,  161.,\n",
       "        171.,  149.,  184.,  155.,  276.,  224.,  213.,  279.,  268.,\n",
       "        287.,  238.,  213.,  257.,  293.,  212.,  246.,  353.,  339.,\n",
       "        308.,  247.,  257.,  322.,  298.,  273.,  312.,  249.,  286.,\n",
       "        279.,  309.,  401.,  309.,  328.,  353.,  354.,  327.,  324.,\n",
       "        285.,  243.,  241.,  287.,  355.,  460.,  364.,  487.], dtype=float32)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-44-755538327404>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-44-755538327404>\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    yhat = history[-1]\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "from pandas import Series\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "history = [x for x in train]\n",
    "predictions = list\n",
    "for i in range(len(test)):\n",
    "    # predict\n",
    "    yhat = history[-1]\n",
    "    predictions.append(yhat)\n",
    "    # observation\n",
    "    obs = test[i]\n",
    "    history.append(obs)\n",
    "print('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))\n",
    "# report performance\n",
    "mse = mean_squared_error(test, predictions)\n",
    "rmse = sqrt(mse)\n",
    "print('RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1966-01     41.0\n",
       "1966-02     39.0\n",
       "1966-03     50.0\n",
       "1966-04     40.0\n",
       "1966-05     43.0\n",
       "1966-06     38.0\n",
       "1966-07     44.0\n",
       "1966-08     35.0\n",
       "1966-09     39.0\n",
       "1966-10     35.0\n",
       "1966-11     29.0\n",
       "1966-12     49.0\n",
       "1967-01     50.0\n",
       "1967-02     59.0\n",
       "1967-03     63.0\n",
       "1967-04     32.0\n",
       "1967-05     39.0\n",
       "1967-06     47.0\n",
       "1967-07     53.0\n",
       "1967-08     60.0\n",
       "1967-09     57.0\n",
       "1967-10     52.0\n",
       "1967-11     70.0\n",
       "1967-12     90.0\n",
       "1968-01     74.0\n",
       "1968-02     62.0\n",
       "1968-03     55.0\n",
       "1968-04     84.0\n",
       "1968-05     94.0\n",
       "1968-06     70.0\n",
       "           ...  \n",
       "1972-05    212.0\n",
       "1972-06    246.0\n",
       "1972-07    353.0\n",
       "1972-08    339.0\n",
       "1972-09    308.0\n",
       "1972-10    247.0\n",
       "1972-11    257.0\n",
       "1972-12    322.0\n",
       "1973-01    298.0\n",
       "1973-02    273.0\n",
       "1973-03    312.0\n",
       "1973-04    249.0\n",
       "1973-05    286.0\n",
       "1973-06    279.0\n",
       "1973-07    309.0\n",
       "1973-08    401.0\n",
       "1973-09    309.0\n",
       "1973-10    328.0\n",
       "1973-11    353.0\n",
       "1973-12    354.0\n",
       "1974-01    327.0\n",
       "1974-02    324.0\n",
       "1974-03    285.0\n",
       "1974-04    243.0\n",
       "1974-05    241.0\n",
       "1974-06    287.0\n",
       "1974-07    355.0\n",
       "1974-08    460.0\n",
       "1974-09    364.0\n",
       "1974-10    487.0\n",
       "Name: Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977), dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    106.000000\n",
       "mean     173.103774\n",
       "std      112.231133\n",
       "min       29.000000\n",
       "25%       74.750000\n",
       "50%      144.500000\n",
       "75%      271.750000\n",
       "max      487.000000\n",
       "Name: Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977), dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAF5CAYAAADzgXLgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3XecVPW9//HXh7qIAgIKNtSAoEJAQbFgoomVqEjkWrga\n+03MT70J6cUkRlOuJlGvN5oYSywxJMYCSlRsEZGgdCwURRAEpMPS+/f3x2eOOzs7vezs7L6fj8c+\nhj1z5pyzx3XnPd/y+VoIAREREZF0mpX7AkRERKThU2AQERGRjBQYREREJCMFBhEREclIgUFEREQy\nUmAQERGRjBQYREREJCMFBhEREclIgUFEREQyUmAQERGRjHIKDGb2MzPbnfA1K2Gfm81sqZltNrOX\nzKxHwvOtzexuM1tlZhvM7Akz27cYP4yIiIiURj4tDO8CXYCusa+ToifM7PvA9cBXgYHAJmCsmbWK\ne/2dwNnAMODzwP7Ak/lcvIiIiNSPFnm8ZmcIYWWK574B3BJCGANgZpcBy4GhwONm1g64Crg4hDAu\nts+VwGwzGxhCmJTH9YiIiEiJ5dPCcJiZLTGzD83sL2Z2EICZHYq3OLwS7RhCWA+8BZwQ23QMHlLi\n95kLLIrbR0RERBqYXAPDm8AVwJnAtcChwOtm1hYPCwFvUYi3PPYceFfG9liQSLWPiIiINDA5dUmE\nEMbGffuumU0CFgIXAnOKeWHxzKwTHlI+AraW6jwiIiKNUBVwCDA2hLA634PkM4bhUyGEajN7H+gB\nvAYY3ooQ38rQBZge+/cyoJWZtUtoZegSey6VM4HHCrlWERGRJu4S4K/5vrigwGBme+Jh4eEQwgIz\nWwacCrwde74dcBxwd+wlU4GdsX2eju3TC+gGTExzqo8A/vKXv3DEEUcUcsmSxogRI7jjjjvKfRmN\nlu5v6ekel5bub+mV4h7Pnj2bSy+9FGLvpfnKKTCY2W+AZ/FuiAOAnwM7gL/FdrkTuNHM5sUu7BZg\nMTAafBCkmT0A3G5ma4ENwF3AhAwzJLYCHHHEEfTv3z+XS5YctG/fXve3hHR/S0/3uLR0f0uvxPe4\noC79XFsYDsSbMzoBK4E3gOOjPpEQwm1mtgdwL9ABGA8MDiFsjzvGCGAX8ATQGngBuK6QH0JERERK\nK9dBj8Oz2Ocm4KY0z28Dboh9iYiISAXQWhIiIiKSkQKDfGr48IwNSFIA3d/S0z0uLd3f0ivFPV67\ntjjHsRBCcY5UQmbWH5g6depUDbgRERHJwdVXT+PBBwcADAghTMv3OGphEBERacQ2bizOcRQYRERE\nGjEFBhEREclo06biHEeBQUREpBFTC4OIiIhkpMAgIiIiGSkwiIiISEYKDCIiIpJWCAoMIiIiksGW\nLbBrV3GOpcAgIiLSSFVXF+9YCgwiIiKN1Pr1xTuWAoOIiEgjpRYGERERyUiBQURERDJSYBAREZGM\nFBhERESELVvgjTdSP19dDW3aFOdcCgwiIiIV6vHH4ZRTPDgkU10Ne+5ZnHMpMIiIiFSoFSu8MNPq\n1cmfX78e2rYtzrkUGERERCrUmjX+mCowVFfDXnsV51wKDCIiIhVq7Vp/TBcY1CUhIiLSxGXTwqDA\nICIi0sSphUFEREQyUguDiIiIZKQWBhEREckoU2BYv16BQUREpEnbtQvWrfN/JwsM27fD1q2qwyAi\nItKkRetEtGuXPDBEz6uFQUREpAmLBjwedpgCg4iIiKQQjV/o0UOBQURERFKIDwxr1/qYhngKDCIi\nIlKrSyKEmgARUWAQERER1q6F5s3h4IP9+8RuifXr/VGBQUREpAlbswb23hs6d/bvEwNDdTVUVUHL\nlsU5nwKDiIhIBVq7Fjp2hE6d/PtkgaF9++KdT4FBRESkAq1d6y0MCgwiIiKSUtQl0aqVj1NQYBAR\nEZE6oi4J8FYGBQYRERGpI2phAB/4qMAgIiIidWRqYVi/XoFBRESkyYsGPYK6JERERCSJ7dth06bM\nYxjatSveORUYREREKkxUBlotDCIiIpJStI5EYmAIwb/fudNbIBQYREREmrCohSG+S2LbNti82b+P\n1pFQYBAREWnCknVJQE23RLRSpQKDiIhIE5asSwIUGERERCTO2rW+EmWbNv59YmBQl4SIiIiwZk3N\n+AWoCQyrVvlj1MLQYKZVmtkPzGy3md2esP1mM1tqZpvN7CUz65HwfGszu9vMVpnZBjN7wsz2LeRa\nREREmor4ok0Ae+0FLVo00C4JMzsW+CowM2H794HrY88NBDYBY82sVdxudwJnA8OAzwP7A0/mey0i\nIiJNSfw6EgBmtWsxVFdDy5bebVEseQUGM9sT+AtwDbAu4elvALeEEMaEEN4FLsMDwdDYa9sBVwEj\nQgjjQgjTgSuBQWY2ML8fQ0REpOmIX0cikhgY2rf3IFEs+bYw3A08G0J4NX6jmR0KdAVeibaFENYD\nbwEnxDYdA7RI2GcusChuHxEREUkhsUsCkgeGYso5MJjZxcBRwA+TPN0VCMDyhO3LY88BdAG2x4JE\nqn1ERESKauxY+N73yn0VxZE46BEaWGAwswPx8QeXhBB2FPdSRERESue55+DBB8t9FcWRqYWh2Etb\ng3cN5GIAsA8wzezTnpHmwOfN7HrgcMDwVoT4VoYuwPTYv5cBrcysXUIrQ5fYcymNGDGC9gl3YPjw\n4QwfPjzHH0NERJqaVav8k/muXdC8ebmvJn8h1B30CB4YFi4cyZAhI3nrLWjWDIYMgepoykSBcg0M\nLwOfTdj2EDAb+J8QwnwzWwacCrwNnw5yPA4f9wAwFdgZ2+fp2D69gG7AxHQnv+OOO+jfv3+Olywi\nIuKBIQT/dN65c7mvJn+bN8OOHcm7JLZtG84zzwznpJOge3d4+GGYNm0aAwYMKPi8OQWGEMImYFb8\nNjPbBKwOIcyObboTuNHM5gEfAbcAi4HRsWOsN7MHgNvNbC2wAbgLmBBCmFTAzyIiIpJSVNRo1arK\nDgyJ60hEOnXysQs7d5ZmDEOuLQzJhFrfhHCbme0B3At0AMYDg0MI2+N2GwHsAp4AWgMvANcV4VpE\nRESSivr3o8dKFa0jkayFIXq+QQaGEMIXk2y7CbgpzWu2ATfEvkREREouvoWhkqVrYQAPRGWfJSEi\nIlKJtmyBTZv835UeGDK1MKxcCRs2KDCIiIjkLL4botIDQ9TC0KFD7e1RYFi40Ad3KjCIiIjkKD4k\nNIbAEC02FS9qcViwwB8VGERERHIUhYSuXSs/MCSr8gi+2FS7djB/vn9fzKWtQYFBRESagCgkHH54\n5QeGZFUeI5071wQGtTCIiIjkaNUqaNUKDjmk8gNDqhYG8HEMH37o/1ZgEBERyVFUrGmffSq/DkO6\nFoZOnWDpUv+3AoOIiEiOVq/2wNC5c+W3MKxdm76FAXytjLZti3teBQYREWn0ohaGzp39DXfnznJf\nUf6SLTwViQJDu3bw6RKRRaLAICIijV58YICa4keVKFOXBBS/OwIUGEREpAlYtcrfTKPAUKndErt3\nZ9clocAgIiKSh6iFIXpDrdTAsH69V3HMpkui2BQYRESkUQuhbpdEpQaGVOtIRNTCICIikqfNm2Hr\nVg8LHTpAs2aVGxhSrVQZUWAQERHJUxQOOnf26YYdO1Z+YFALg4iISJFFhZqi7ojOnSu3eFPUJaEW\nBhERkSKLb2GIHiu5haFZM1+tMpm2baGqqu7S18XQIvMuIiIilSsKB9Gn70oODFHRpmYpPu6bwd/+\nBgMHFv/camEQEZFGbdUq/9S9xx7+fSUHhnRFmyLnnQf77Vf8cyswiIhIoxZNqYxKJVd6YEg14LHU\nFBhERKRRiwJDpFOnyg0M6daRKDUFBhERadQSA0Pnzl4xcfv28l1TvrLpkigVDXoUERFCKP7qhg3F\nqlXQpUvN91F4WL06u77+devggw9g3jz/mj8fhg+HM84ozfWms2oVHHlk/Z8XFBhERJq8jRvhkEPg\n4Yfh7LPLfTXFt2oV9O5d830ugWH6dDj2WNi1y7/fZx//96JF5QkMS5fC/vvX/3lBXRIiIk3eJ5/4\nm+cPf+irITY2q1fX7ZKA7MYxPP201zyYMsVbGlasgJ/+FCZMgC1bSnO9qWzd6j/LAQfU73kjCgwi\nIk1cVD3wnXfgiSfKey3FFi08FdVggNwCw/PPe0vCgAE11RNPOw22bfPQUJ+WLvVHBQYRESmLaH2C\nvn3hpptqmt8bg40bfXBjfAtD+/a+pkSmwLBihbcsDB5ce/uRR0LXrvDyy8W/3nSWLPFHBQYRESmL\nKDDceSfMnu2VAhuLxLLQ4IM7s6nFMHasP551Vu3tZnDqqQoMIiLSxKxdCy1awCmnwLnnws9/Djt3\nlvuqiiNZYIi+zxQYnn8e+vf31oREp50G06bVdOfUhyVLfK2IVOtIlJoCg4hIExfN7TfzsPDBB/Do\no+W+quJIFRgyFW/atctbGBK7IyKnnurjI/71r+JcZzaWLPHWhXJNf1VgEBFp4uKLAR19NJx/Ptx8\nM+zYUd7rKobEhacimVoYJk/21oNUgeGgg6Bnz/rtlli6tHzdEaDAICLS5CWuT/Dzn8PChfDnP5fv\nmopl1Spvxm/Tpvb2zp19imIqzz/vS0Qfd1zqfU47rX4DQ9TCUC4KDCIiTVxiueE+fWDoULjvvvJd\nU7GsXl23dQEytzA895xPp2yRprzhaad55ceFCwu/zmwoMIiISFklW5/gjDO8yuGmTeW5pmJJXEci\nki4wpJpOmeiUU6BZM3jllYIvM6MQ1CUhIiJlliwwDBrkA/8mTSrPNRVLusCwcaNXT0yUajplor33\n9oJO9dEtsXq1F4tSYBARkbJJFhh69/YCR/VdzbDY0gUGSD6OId10ykSnneYtDCEUdp2ZlLsGAygw\niIg0eWvW1A0MzZrBCSc0/sCQ2C2RaTplotNO8y6Md98t7DoziQJDuRaeAgUGEZEmbccOb5pPDAzg\n3RITJ1b2glS5BoZM0ykTnXgiVFWVvltiyRKvv5BNq0epKDCIiDRh69b5Y6rAUF0N771Xv9dULNHC\nU8kCQzRzIjEwZDOdMl5VFZx0Uv0Ehi5doGXL0p4nHQUGEZEmLFpHIllgGDjQF2mq1G6J6mrvYkgW\nGPbay9984wNDCPD443D22emnUyaKxjHcdFPNipLFVu4ZEqDAICLSpKULDG3beuXHSg0Mqao8Qs0C\nVPGDHt96C+bMgcsvz+08/+//wVVXwW9/CwcfDBddBK+/XtyBkOWuwQAKDCIiTVq6wADeLVGpgSEK\nA8laGKLt8S0MDz3kJZ+/+MXczrPXXnDPPf6mfvvtMHMmnHwyPPZYXpedlAKDiIiUVTaBYcEC+OST\n/I5/xRX1u0BTvFQLT0XiA8OWLb6s92WXeTdMPtq3hxtu8CXCjz4aXn01v+Mko8AgIiJlFS1t3bZt\n8ucHDfLHfFoZtm2Dhx/2T9/lkK5LAmoHhtGjfcxDrt0RyZh5YJg5s/Bjgd/HVasUGEREpIyihadS\nLZm8//5wyCH5BYZoAODYsbB9e96XmLdVq7y7oHXr5M/HB4Y//9nD0WGHFefc/fr57JKdOws/VnQf\ny1mDARQYRESatGRVHhPlO45h8WJ/3LABxo/P/fWFSjWlMhIFhsWL4aWX4Mori3fufv28ZWDu3MKP\n1RCqPIICg4hIk5ZtYJg+HTZvzu3YUWDo3BnGjMnv+gqRKTB06uT7PPqo11O44ILinbtvX38sRreE\nAoOIiJRdsrLQiQYN8qb1XBeiWrzYBwIOGwbPPlv69RYSrVqVevwCeJjYsgXuvdevsV274p17772h\nWzd4++3Cj7VkCeyxh9/LclJgEBFpwrJpYejd299Mc+2WWLwYDjwQzjkHPvwQ3n8/u9e99JIPQixU\nNl0SAAsXFrc7ItK3b3FaGKKiTanGmdQXBQYRkSYsm8DQvHl+C1F9/LEHhi9+0Zv8n302u9f94hfw\nq1/ldq5kVq/OLjB06wannFL4+RL161e8Lolyd0eAAoOISJOWTWCA/BaiiloY9tjDyydnM44hBG/G\n//jj7M+TysqVmbskwKdSNivBu2G/fl6/YuXKwo5TkYHBzK41s5lmVh37+reZnZWwz81mttTMNpvZ\nS2bWI+H51mZ2t5mtMrMNZvaEme1bjB9GRERyk21gOOEEX6jqgw+yP3YUGMC7Jd54o6ZQVCoff+zn\nWbassKmYS5Z4l8Thh6fep1s3b8244Yb8z5NOv37+WGgrQ0UGBuBj4PtAf2AA8Cow2syOADCz7wPX\nA18FBgKbgLFm1iruGHcCZwPDgM8D+wNPFvAziIhIHtItbZ0oqk/w0UfZH3vZsprAcPbZvhDU2LHp\nXxe9uYZQ2EJO0TTOz30u9T5m8OMfwz775H+edLp399aVQgJDCBUaGEII/wwhvBBC+DCEMC+EcCOw\nETg+tss3gFtCCGNCCO8Cl+GBYCiAmbUDrgJGhBDGhRCmA1cCg8xsYJF+JhERyUK6pa0T7b+/N9sv\nWpTdsT/5xN/sosBw4IFw1FGZuyXi31wL6ZYYP95DTpcu+R+jUM2bw2c/W1hgWLPG6zmUu2gTFDCG\nwcyamdnFwB7Av83sUKAr8Eq0TwhhPfAWcEJs0zFAi4R95gKL4vYREZF6kGkdiXgtW/qbVraBIarB\ncNBBNdvOPReefz599cOZM2HAAP93oYEhXetCfSl04GNDqcEAeQQGM+tjZhuAbcA9wJdjb/pdgQAs\nT3jJ8thzAF2A7bEgkWofERGpB7kEBvA+/1wDQ9TCAD6OYc0aHzyZysyZcNJJXnMg38Cwdi28+27D\nCQyzZ+c/HqOiAwMwB+iHj1H4A/CImaUZViIiIg1RKQPDxx/DnnvWLoZ0zDGw776puyU2bYJ58/xN\ntlu3/APDv//t3SENITD07evjOebMye/1S5b4WIv99ivudeWjRa4vCCHsBObHvp0eG3vwDeA2wPBW\nhPhWhi7A9Ni/lwGtzKxdQitDl9hzaY0YMYL2CaWuhg8fzvDhw3P9MUREmrwoMHTsmN3+3bplX+0x\nmiERX2yoWTMf/DhmDNx6a93XvPOOv9H36+ddGfkGhvHj/Q32M5/J7/XFFF8iOvp3LpYs8ZDVsmV2\n+48cOZKRI0fW2lZdXZ37iZPIOTAk0QxoHUJYYGbLgFOBt+HTQY7HAXfH9p0K7Izt83Rsn15ANyBN\nI5W744476N+/fxEuWURE1qzxN6I99shu/+hT/+7dmesWxE+pjPelL/nKkAsXwsEH135u5kwfKHjk\nkR4Yci1FHYnGL5S7MiJ4C8uhh/rP9pWv5P76qMpjtpJ9iJ42bRoDooEhBci1DsOvzOxzZnZwbCzD\nr4GTgb/EdrkTuNHMzjWzzwKPAIuB0fDpIMgHgNvN7BQzGwA8CEwIIeT5qyEiIvmIajBk+8barZs3\nr69YkXnfVIHh9NM9FDz/fN3n3n4bevXyqpD5tjBs2QKTJzeM7ohIIQMfG8qUSsh9DMO+wMP4OIaX\n8VoMZ4QQXgUIIdwG/B9wLz47og0wOIQQP9xjBDAGeAJ4DViK12QQEZECTZ0KQ4dmV5Ex26JNkW7d\n/DGbcQypAkP79l41MllgmDmzptjRQQd54aUtW7K/PvBWiR07GmZgyGfxrYoNDCGEa0IInwkhtAkh\ndA0hfBoW4va5KYSwfwhhjxDCmSGEeQnPbwsh3BBC6BxC2CuEcEEIIYu8KiIimTz1lC/ctGZN5n1L\nFRh27vQ6DMkCA8DgwfDKK15fILJ7t7cwxAcGqJltka3x4z2U9OmT2+tKqV8/Lw+9LONIvbqWLGkY\nNRhAa0mIiDQqU6f64/LECe5J5BoYOnTwmQ+ZAsPy5V7VMV1g2LTJS0VHPvoINmyoGxhy7ZYYP95b\nMJo3z+11pZRvieht2zxoVGQLg4iIlM7GjYW9PoSawJDNOINcA4NZdlMrozf5+KJN8fr29U/N8d0S\n0Ztp9OYahY1cAsPOnT6lsiF1RwAccgjstVfugeGTT/xRgUFERD61aJFPn3vttfyP8fHH3u8PpWlh\ngOwCQ7KiTfHM4Kyz6gaGffaBrrESflVV/n2qc40bB9On1942c6aHrpNOyvxz1KdmzfIrER2FJQUG\nERH51MMP+wC/qIUgH9Om+aNZaVoYIPvA0KZN+mMPHgyzZvn0SqgZ8Bg/YyPdTInLL/euh1fjRtGN\nHw+tW8Oxx2b3s9SnfGZKvP++34/u3UtzTblSYBARKbMQ4KGH/N+5LB+daOpUX2zpoIPK38KQWLQp\n0Wmn1Z5emaywUarAsHq1B4299/ZCUC++6NvHj4eBAz00NDSf/awHgFxKRM+d690ZbdqU7LJyosAg\nIlJmb7wB8+f7G2ShgaF/fw8NmQJDLktbx+vWzQfipZvumGpKZbwOHWqmV65fDwsW1IxfiKQKDDNm\n+ONzz8Gpp8KQIfDPfzacBaeS6dPHx1i8/372r5kzBw5vQAsvKDCIiNSDNWtS10Z46CGvBnjppbm9\nocSLBjwOGOBjITJ1SeSytHW8bGYvZBMYoGZ65ZQp/n22gWHaNGjb1j+1P/mkj4c47zwPMg01MPTu\n7Y/vvpv9axQYRESaoN694Vvfqrt90yZ4/HHvk+/Z099sN2/O/fhLlnhIGDAguxaGqE5DPi0MkL5b\nIpfAsGkT3HOPl6g+4ojazx90kLc+rE9Y33j6dA8XzZp598Pjj3tgaNsWTjwxt5+nvnTs6OtbvPde\ndvtv3+6tTr16lfa6cqHAICKSgxDqjs7PZP16L9pz110wYULt55580rsGosAA8OGHuV9XNOAxCgyZ\nWhhyXXgqcsABPjYhVWDYtcvDSzaBIZpe+dRTHhZatar9fKrWjOnTvesl0qoV/OMf3q0RvzpmQ9On\nT/YtDB9+6PdSLQwiIhVqyhR/s5o9O/vXRNMMO3aEa66BrVtrnnvoIfjCF3xw22GH+bZ8uiWmTvVp\niAce6F0Sy5enL0Wc69LWkdatfepjqsCwYoX31aeqwRAvml4ZrVCZKFlg2LjRBwMefXTtfZs185+/\nIevdO/sWhmg5bAUGEZEKFTX15xIYlizxx4ce8k+Ov/iFf//RR/Cvf8EVV/j3nTt7WeN8Bj5GAx7N\nvIVhy5b0haDyDQyQfqZEphoMiQYP9sdkgWH//f3niQ8Mb7/tAaMSFy7u0wfmzctufYw5c3xg6L77\nlv66sqXAICKSg2iwYC7dBtGb6BlnwI9/DLfe6iP9H37YSy0Piy2/Z+atDPkGhmgF4+hNJl23xNq1\nuS1tHa+YgeGMM/zN//TT6z7XsqX3+8cHhmnTfPuRR+Z2zQ1B794edqLWg3TmzPHxCw1hie6IAoOI\nSA6qq/0x18Cw777e1/7DH3oz89VXe2C48EIfrBfp2TP3LolPPvExElFg6NLFH9MNfMx1aet4mQJD\nq1beWpKNdu087CTWYIgkzpSYPt1nRySOd6gEUcjJZhzD3LkNqzsCFBhERHKST2CIHwTYqhU88IC3\nMCxYUNMdEcmnhSGqDplrC0M+3RFQExiSjZHIpmhTLhIDw7RpdccvVIp27fzeZRrHELVCKDCIiFSw\nfFsY4pvoBw6En/wETjih7roHhx3mLQOJUwnTmTrVB1RGUx47dfJBgNm0MOSjW7ealRQTZTulMlvx\ngWHbNn+zrdTAANnNlFi+3H/PFBhERCpYFBgWLsy+zO/ixXUXELrpJp9imfhJPJpaOW9e9tcUjV+I\njtW8uc8YKGULAyTvlvj449IEhhA8LOzYUZkDHiN9+mRuYZg71x8bUg0GUGAQEclJdbUPutu9u2bh\npExSfepO1myfz9TK+AGPkWhqZSqlCgylaGHYssULTU2f7vcs1XiHStC7t8+OSTeDZc4cD30NZdGp\niAKDiEgOqqv9UyJk1y2xdasvlpTtm2iHDj5gMNtxDMuWwdKldQNDpmqPhQSGTp18QaTEwLB7d/ZF\nm7IVX4th+nRvpo8fJFppot+dWbNS7zNnjoeFhjawU4FBRCQHUWBo2TK7wBDVYEjskkinZ8/sA0N8\nhcd4mdaTWLMm/8BglnymxKpV3k2TTdGmbMUHhkoe8Bg5/HC/f+nGMTTEAY+gwCAikpN163yA4aGH\nZhcYcq1LAN4tkW2XxNSp/sZ/yCG1t5eyhQGSB4Z8ftZMunSBFi28GX/mzMoevwBe96J79/SBoSFO\nqQQFBhGRnFRXezXG7t2zG5iYTwtDLlMr4ys8xkvXwrBjhy/6VOzAEM1mKGZgaN7cj/fqq74oV6W3\nMED6EtFbtng4amgDHkGBQUQkJ/GBIdsWhg4dvKJjtnr29C6D1asz7/v223DUUXW3d+nirQjJZnLk\nu/BUvMTAsHMn/OY33vJS7HLGBx0EL77o/072s1aadFMrP/jAZ4SohUFEpILt2uWj26PAMH++D/RL\nJ9mUykyimRKZWhk2b/ZPo717130uXfGmQtaRiHTr5l0e0UJav/oVTJwIjz7qNSCK6aCD/Gc95JDC\nQk5D0bu3D1SN/jvEi8pGq4VBRKSCRcWU2reHHj38zfKTT9K/Jp9ZAz16+GOmwDB3rn8aPeKIus9F\n5aFLGRjAA9HEiXDzzXDjjTBoUP7HTCUa+NgYuiOgZqZEsm6JuXO9hkanTvV7TdlQYBARyVJUtClq\nYYDM3RL51CXYc09fqTFTYIim5qULDMkGPhYzMLz7LlxySU31ylKIAkOlD3iM9OrlAzmTBYZo0amG\nSIFBRCRL8YHh0EN9oGE2gSHXLgnIbqbE7NkeLNq3r/vcPvv4Y6laGKIQ9PWv+3TKxx7zN8FSaGwt\nDK1a+X/fZOMYGuqUSlBgEBHJWnxgqKryIJBupsTOnV5YKZ9ZA9nMlJg1K/Uyz1VVfp2pWhjyXdo6\n/vhduvjPd889HqBK5fjjYciQuutuVLJkJaJDaLhTKkGBQUQka+vW+WP0iT7TTIlly3xQZCGBIdmK\nkJHZs1MHBkg9tbKQpa3jDRjgq21ecklhx8lk331h9OjkLSmVqnfvui0MS5b4dFcFBhGRChe1MHTo\n4I+ZAkNUyCifLomePWHDhtTFl7Zv90CRbPxCJFXxpjlzitMi8Oyz8OCDxVvKuinp08dX+4wPdNEM\nCQUGEZH/ySQ+AAAgAElEQVQKV13t/c9VVf59jx7ZBYZ8WxggdbfEvHk+zTNTC0OywDB5Mhx3XO7X\nlKhZM4WFfEUzJb72NR//sXKlB4ZWrepW7WwoFBhERLIUFW2KdO/uzftr1iTff8kSX6Qpn8GF3bv7\nm3GqwJBuhkSkS5e6XRJr1/pgymOPzf2apHh69vQlzj/6CC691P9b3XijB8Xmzct9dckpMIiIZClZ\nYIDUrQzRDIl8PoVXVfnUxVQzJWbP9lUto9kQySTrkpgyxR8HDsz9mqR4zOBnP/MVOJcuhT//Gb70\nJbj22nJfWWolmgQjItL4pAsMyT6x51ODIV66qZWzZqVvXQDvkli50gdeRtUXJ03yMRhRcSgpv/32\ng8sv96+GTC0MIlJWCxbAc8+V+yqykxgYOnTwUsXpWhgKCQzHHANvvJG8/HSmGRLgLQy7dtXuMpk8\n2cNNscs3S+OnXxkRKas77vBpeemmDzYUiYEB0s+UWLIkvxkSkcGDvYVg6tTa23ft8gFy2bQwQM04\nhhDgrbfUHSH5UWAQkbKaM8frG6xaVe4rySxZYEg1U2L37vzWkYh3wgnQrh08/3zt7R99BNu2ZdfC\nADXjGJYs8doQGvAo+VBgEJGyiuaez51b3uvIxrp1yVsYklV7XLXKayUUEhhatoTTT68bGKIZEpkC\nQ9TCEAWGSZP8US0Mkg8FBhEpm40b4eOP/d+VEBiqq2uKNkW6d/dR7lu21N6+ZIk/FtIlAT5y/q23\nYPXqmm2zZsFee/k6Eum0awetW9d0SUya5AFmv/0KuyZpmhQYRKRsohkAZpUTGJK1MADMn197eyFF\nm+KddZaPPXjxxZpt0YDHTNM1zWpPrZw8Wa0Lkj8FBhEpmygkHH98ww8MO3d6nf9UgSFxHMPixb56\nY9QtkK/994d+/WrPJMlmSmUkWk9i9+6aGRIi+VBgEJGymTPHm8ePPbbhB4b16/0xMTDst59Xc0wM\nDEuW+HPFqNo3eDCMHetv+iFkN6UyErUwzJ3ra1OohUHypcAgImUzZw706uVfH34IO3aU+4pSi1/a\nOp6ZF1h6/fXa2wutwRAvfnrl4sU+9iOXFobly338gpmvMCmSDwUGESmbOXN8Zb5evbzJf8GCcl9R\naqkCA8B3vgOjRsGYMTXbihkY4qdXzp7t23JpYVixwrsjDj+8cS0RLfVLgUFEymL3bh/0GAUGaNjd\nEukCw6WX+uDEa6+t6booZmCIn145a5avM3Hwwdm9NmphUMEmKZQCg4iUxaJFsHWrB4YDDoC2bRt2\nYFi3zh+TBQYzuPdeDxXf+56PM4gWniqWwYP9TX/8eL9n2Y6N6NLFp3xOn64Bj1IYBQYRKYuoYFOv\nXv6G27Nnww4M6VoYwFeWvPVWDw7PPOMzKorVwgA10ytHjcq+OwJqqj3u2qUWBimMAoOIlMWcOTVL\nOIMHhlQrMzYE1dVeBKmqKvU+114LJ50EV1zh3xczMBxwAPTt61052Q54hJppna1a+etF8qXAICJl\nEc2QiFZN7NWr4bcwZBow2KwZ3H9/TdXHYnZJgFd9hPxaGI46ygOPSL4UGESkLKLAEOnVywfnRU3/\nDU02gQH85/jFL3xWQ6bSzbn68pe9paB//+xf06mTBxl1R0ihFBhEpCzmzvXBe5GGPlMi28AAPs1y\nyRJ/cy+mgQNhzRo45JDsX9O8OXz723DZZcW9Fml6FBhEpN6tW+fLLMcHhp49/bExBAaAPfcszXW0\nbZv7a267TTMkpHA5BQYz+6GZTTKz9Wa23MyeNrOeSfa72cyWmtlmM3vJzHokPN/azO42s1VmtsHM\nnjCzAiuui0iliEJBfGCIVl9sLIFBpLHJtYXhc8D/AccBpwEtgRfNrE20g5l9H7ge+CowENgEjDWz\n+Ma5O4GzgWHA54H9gSfz/BlEpMJEUyp7JnzcaMgDHxUYpKlrkcvOIYQvxX9vZlcAK4ABwBuxzd8A\nbgkhjIntcxmwHBgKPG5m7YCrgItDCONi+1wJzDazgSGESfn/OCJSCebOhYMOqtu83qsXTJhQnmvK\nZN06BQZp2godw9ABCMAaADM7FOgKvBLtEEJYD7wFnBDbdAweVOL3mQssittHRBqxaA2JRL16wQcf\neK2Bhqa6Gjp0KPdViJRP3oHBzAzvWngjhDArtrkrHiCWJ+y+PPYcQBdgeyxIpNpHRBqxxCmVkV69\nvFz0okX1f02ZqEtCmrpCWhjuAY4ELi7StYhIE7BjB8ybl7qFARreOIYdO2DzZgUGadpyGsMQMbPf\nA18CPhdC+CTuqWWA4a0I8a0MXYDpcfu0MrN2Ca0MXWLPpTRixAjaJ/wfO3z4cIYPH57PjyEiZbBg\ngb8BJwsMBx/stQvmzoUzz6z/a0slWoFSgUEaupEjRzJy5Mha26qLVA0t58AQCwvnASeHEGo1HIYQ\nFpjZMuBU4O3Y/u3wWRV3x3abCuyM7fN0bJ9eQDdgYrpz33HHHfTPpcSZiDQ4yaZURpo3hx49Gt6a\nEpkWnhJpKJJ9iJ42bRoDBgwo+Ng5BQYzuwcYDgwBNplZrEo51SGErbF/3wncaGbzgI+AW4DFwGjw\nQZBm9gBwu5mtBTYAdwETNENCpPGbM8eLGqUqm9wQp1YqMIjk3sJwLT6o8bWE7VcCjwCEEG4zsz2A\ne/FZFOOBwSGE7XH7jwB2AU8ArYEXgOtyvXgRqTzRgEez5M/36gWPPVa/15SJAoNI7nUYshokGUK4\nCbgpzfPbgBtiXyLShKSaUhnp1Qs+/hg2bcqvDHIprFvnjwoM0pRpLQkRqTchpJ5SGYme++CD+rmm\nbKiFQUSBQUTq0cKFvtri0Uen3ufII32mxAsv1N91ZVJdDVVV0Lp1ua9EpHwUGESk3kyZ4o/pBmy3\nbw+XXw533AFbttTPdWWiok0iCgwiUo+mTIEDDoD99ku/3/e+B6tWwYMP1s91xZszx7tO4ikwiCgw\niEg9mjIFjj028349esBFF8FvfuNFnurL4sXQuzc8mbB2rgKDiAKDiNST3bs9MBxzTHb7/+AHPuYh\noWhdSU2a5Nf5yiu1tyswiCgwiEg9+fBDf+PNNjD07Qvnngu//nX9rV45ebI/jhtXe7sCg4gCg4jU\nk2wGPCb60Y98TMGoUbW3z5sHd94JO3cW7/rAr7FNG5g9G1asqNmuwCCiwCAi9WTKFDj0UOjcOfvX\nHH88fOEL8Ktf+UDENWtgxAifejliBLz5ZvGuLwS/xssv9+9ff73muXXrFBhEFBhEpF5Mnpx9d0S8\nH/0Ipk6Fr3/dB0Pefz/85Ce+UNV77xXv+ubP92AwdCh07167W0ItDCIKDCJSD3btgmnT8gsMp57q\nMyvuuw8uvNC7I37yEzjsMHj33eJdYzR+YcAAOPnkuoGhQ4finUukEuW8vLWISK7mzvW1IbKZUpnI\nDJ55BjZu9BaGSO/eubcwrF4Ne+8NzZJ8VJoyBQ45xLtMTj7Za0CsXg3t2nkBKbUwSFOnFgYRKblo\nwGP//vm9vmvX2mEBoE+f3FoY5syBbt3gt79NfY1RC8jJJ/vj+PFaR0IkosAgIiU3eTL07FncN93e\nvWHlytqzGVLZtg2GD4fNm5Mvnb17t4+TiFpADj7Yv8aNU2AQiSgwiEjJ5VKwKVt9+vhjNt0SP/4x\nzJoFN94Ib7/t4yDizZ3rXR7x1xiNY1BgEHEKDCJSUjt2wIwZ+Y1fSKdHD1/VMlNgeOkl+N3vvADU\nD37gdRYSSz8n6zI5+WS/7oUL/XsFBmnqFBhEpKRmzYKtW4vfwtCyJfTqlX4cw6pVXlfhjDPgm9+E\ntm1h8ODkgeGww2rPhDj5ZK/N8M9/+vcKDNLUKTCISElNnuyzEo4+uvjH7tMndQtDCHD11bB9Ozz0\nUM3MiGHD/JoWLarZN9miWJ/5DBx4IDz7rH+vwCBNnQKDiJTUlClembFt2+Ifu3dvb2FIXI4avJz0\nM8/49Mj45bTPOce7Mp56yr/fuROmT6/bAmLmrQwrVkBVlb9GpClTYBDJYO3a5G9Ikp1SDHiM9Onj\n1Rk/+aTuc089BUcdBUOG1N7erh2cfnpNt8SsWV5nIdk1RtMrVbRJRIFBJK1XXoF994UXXyz3lVSm\nbdt8VkKpAkPv3v6YOI5h924YO9bHKyQzbBhMmADLlnmgSdVlEgUGdUeIKDCIpPTRR3DRRd5kHb8Q\nkWTvnXd8lkSpAsOhh/qsh8RxDFOneo2GVIFhyBAPCU8/7YHhiCNgzz3r7nfYYV40SoFBRKWhRZLa\nsgXOP9+br3v3hkmTyn1Flemtt3w2Q79+pTl+8+b+Zp/YwvD88/4mf8IJyV/XqZOvgvnkk15nIVWg\nMfNwsXFjca9bpBKphUEkQQhw7bVeSvjpp31K3pQp3swtuRk/3t+Mq6pKd45kMyWee87HKbRI85Fo\n2DB47TWYOTN9C8g998Bf/lKUSxWpaAoMIgnuvhseecSXUe7XDwYO9IF1idUBJb0QPDB87nOlPU8U\nGKKBqatWeYtQqu6IyNChHgJ37EhfVKp5c29pEGnqFBhE4kycCCNGeJGf//xP3xZ9+lS3RG4WLICl\nS0sfGHr39i6DqK7Ciy96eDjrrPSv69rVr61FC+jbt7TXKNIYKDCIxLn9djj8cLjttppte+/tCycp\nMORm/Hj/ZD5oUGnPE60pEY1jeP55n065//6ZX/ud78B11/nASRFJT4FBJGbrVn+zGT7cB+rFO/ZY\nBYZcjR/vb+Z7713a8xx0EOy1l3dLZJpOmejcc+HOO0t7fSKNhQKDSMyrr8KmTXDeeXWfGzjQFyLa\nvr3+r6tSjR8PJ51U+vOY1VR8zDSdUkTyp8AgEjNqlK+AeOSRdZ8bONCLEL3zTv1fVyVavhzef7/0\n4xcivXt7C0Om6ZQikj8FBhG8KfuZZ7x1IdmI+KOO8sFx6pbIzhtv+GN9BYY+fbzE85gxmadTikh+\nFBikUdi928s457vmw1tv+afioUOTP19V5SPpkwWGGTOgWzdYsiS/czdGb7wBhxziqz3Wh969fQzK\n5MnqjhApFQUGaRTGjoXTTst/zYfRo2GffdI3ZQ8cmDww3HorfPyxj4EQVx/1F+JFMyUg83RKEcmP\nAoM0CuPH++NDD+X3+lGjfMR88+ap9xk4EGbPhg0barYtWgT/+If/e8KE/M7d2GzY4MtF12dg6NoV\nOnb0QlvZTKcUkdwpMEijMGGCT4V8+mmvypiLOXNg7tzksyPiDRzoXR5Tp9Zsu+suX29i+HAFhsjE\nid5FVJ+BwQyuuAKuv77+zinS1CgwSMXbvt27Cr7xDS/z+/e/5/b60aNhjz18sFw6hx/uKxpG3RLr\n18N998HXvubrTbz3Xu5hJZ1t2+B734NPPineMevD+PHevdOrV/2e93e/g2uuqd9zijQlCgxS8aZP\n9wFv//Ef3n+da7fE6NH+hp+p2l/z5jBgQE1geOAB2LzZP9UOGuStDxMn5vUjJPWHP8BvfgMPPli8\nY9aHqP6C1l8QaVwUGKTiTZjgsxiOPtqbpd9807sZsrFsme+fanZEomjg486d8L//CxdfDAcc4PUb\n9tmneN0S69bBLbd4SBk9ujjHrA/btvmMk/rsjhCR+qHAIBVvwgR/I2/Vygcu7r139q0Mzz7rn4TP\nPju7/QcO9BkRf/gDLFwI3/qWb4/WTChWYPjVr/zN93/+x6cKVsqUzalTvbVHgUGk8VFgkIoWAvz7\n3zULHFVV+SqTjz4Ku3Zlfv2oUd583rlzducbONAff/hD+MIXvFUjMmiQf7resSO3nyHRwoU+mPI7\n34Grr/ZWhmeeKeyY9WX8eGjb1gtdiUjjosAgFW3BAu9WiF8R8YorfFnll15K/9qtW712wpAh2Z/v\noIOgSxdfcyJqXYgMGgRbtnghp0LceCN06OCBYe+94eSTK6dbYvx4r2WhSosijY8Cg1S0qAsgvuDS\ngAFe+S9Tt8SkSR4avvjF7M9n5ufq1Qu+9KXaz/XvD61bF9YtMW0a/OUv8POf+4wM8PEVr74K1dX5\nH7c+7NwJr7/uAUdEGh8FBqloEyb4YlEdO9ZsM4Mrr/TuhrVrU7923DhfqKhv39zO+Yc/eOtFs4T/\ne1q39mWw8w0MIcB3v+vTN6++umb7kCHezfHCC/kdt75MnuxFmzJNTxWRyqTAIBVtwoTa3RGRSy7x\nT7x/+1vq144b54Pz0lV3TKZrV++aSCYa+JjPmhZjx3pLwq231m7SP/hgHysxalTux6xPr7ziRawG\nDCj3lYhIKSgwSMVat86LJSULDF27+ifdqGxzou3bfbBksZvPBw3yQksffZT7a0eN8taFc8+t+9zQ\nofDcc37dDdXLL/tAUI1fEGmcFBikYk2c6J/kkwUGgC9/2fvUV6+u+9yUKT5AsdiB4cQT/TGfbolZ\ns3x2QbKCR+ed55UlX3utoMsrmU2bPICddlq5r0RESkWBQSrWhAleLKl79+TPn3uuT6385z/rPvfa\na7DXXrWnRRZDp07eSpBrYAjBW0uOPDL58337+nLRDXW2xPjxPs5CgUGk8VJgkIoVjV9IVYJ4v/3g\n+OOTv8mOG+evLUXzeWIBp1274Le/hXPOSV0bYsUKWLPGZ3ckY+atDKNH5zc+otReftlXiazv9SNE\npP4oMEhF2rHDiySl6o6InHeezy7YsqX2aydMgFNOKc21DRoE777rYywWLYJTT/XZD//8J7z/fvLX\nvPeeP6YKDODjGJYsqb1aZqksWwY/+pEHmWy88oq3Lmj9CJHGS4FBKtKMGR4CMgWGoUN9gahXXqnZ\nNm2a97mXql5AtBDVj37kXQnz58NTT/lz06cnf82sWV7aOlX3CnhFyo4dS98t8fTT0KcP/PrXvhpn\nJitX+n8PdUeING4KDFKRJkzwugf9+6ff7/DDoWfP2lMSx43z8sWlmv532GE+tuIPf/BuiLff9gGY\nhxziYSWZ997z5vx0XSQtWnhNhpEjYffu4l/3hg1w1VVw/vk+3XTwYHjiicyve/VVfzz11OJfk4g0\nHDkHBjP7nJk9Y2ZLzGy3mdUprGtmN5vZUjPbbGYvmVmPhOdbm9ndZrbKzDaY2RNmtm8hP4g0LRMn\nwjHHeGjIZOhQX2QqGj8wbpzPZmjZsjTXZgZ33w1PPulVGzt08O39+6duYUg34DHe1VfDhx8Wf7bE\n9OnQr59PQ33gAW8RufJKbzmYPz/9a19+GY44wscwiEjjlU8LQ1tgBvD/gDrDr8zs+8D1wFeBgcAm\nYKyZtYrb7U7gbGAY8Hlgf+DJPK5Fmqjp0z0wZOO887wv/s03PTS88UbpyxdfcIF/Uo939NHewpA4\naDGaIZFu/EJk0CB/c/7Tn4p3rfPmwZln+roVM2Z4K4OZtzBUVXnwSScavyAijVvOgSGE8EII4ach\nhNFAsiFO3wBuCSGMCSG8C1yGB4KhAGbWDrgKGBFCGBdCmA5cCQwys4H5/iDSdGzY4G9y2a6IeNxx\nvmDU6NH+hrh+fXnWOzj6aB8IuXBh7e3RDIlsWhjM4L/+y8cZrFxZ+DUtX+5hoVMnePHF2mMo9twT\nzjorfWCYP98XAFNgEGn8ijqGwcwOBboCnw4xCyGsB94CouWBjgFaJOwzF1gUt49ISu+845/Ksw0M\nzZt7TYZRo7w7oqrK13yob9F4i8RuiVmz/DGbFgaAr3zFHx95pLDr2bgRzj7bB4++8IKHhkTDhvls\nlMWLkx/j5Zf9/mrBKZHGr9iDHrvi3RTLE7Yvjz0H0AXYHgsSqfYRSWnGDB9/kM0n8sjQofDBB3D/\n/b7aZDZjH4ptv/28pSNx4ON77/nPk26GRLzOnf2N/L778q/JsGMH/Md/+DTP557z9SqSOfdcv7Zo\nlkeil1+GgQN9ES8Radw0S0IqzowZHhZatcq8b+TUU31mxOzZ5f00nGzg46xZPkMil0GYX/0qzJ3r\nFRbz8fWv++yGUaPSt9S0b+9rciTrlti924+h2REiTUOx69wtw8c1dKF2K0MXYHrcPq3MrF1CK0OX\n2HMpjRgxgvYJH2WGDx/O8OHDC71uqSAzZmTfHRGpqvK++qeeKm9gOPpo+POfa2/LdoZEvJNP9umb\nf/oTfP7ztZ+bM8e7F/bZJ/lrFy70mRC//z188YuZzzVsGFxzjY936NKlZvuYMb5Oh8YviDQcI0eO\nZOTIkbW2VVdXF+fgIYS8v4DdwJCEbUvxAY3R9+2ALcAFcd9vA74ct0+v2LEGpjhPfyBMnTo1SNO2\nY0cIVVUh3HFH7q8dNSqErl1D2Ly5+NeVrSeeCAFCWLasZlvnziH8/Oe5H+u220Jo3TqE1av9++3b\nQ/jxj0No1iyEoUPTv66qKoT167M7z6pVITRvHsIf/1izbcqUENq2DeHss0PYtSv3axeR+jN16tSA\nDxfoHwp4z8+nDkNbM+tnZtFnvM/Evj8o9v2dwI1mdq6ZfRZ4BFgMjI4FlPXAA8DtZnaKmQ0AHgQm\nhBAm5Xo90vh8/LGvMpnM3LmwdWt+i0add54vPd2mTWHXV4jouqNuiRUrYNWq3FsYAC6/3LsFHn3U\nWxVOOAFuvdVLXo8Zk7qs89//Dl/6ki++lY1OnfyYUbfE/Pn++t69/VjN1LEp0iTk87/6MXj3wlQ8\nsfwOmAb8HCCEcBvwf8C9+OyINsDgEML2uGOMAMYATwCv4a0Sw/L6CaRR2bwZzjjDp/PFr/8QmTHD\nH/v1q9/rKpZDD/VxAdHAx1xnSMTbd18fzPnrX/vYiI0bvaDV44/79Mu//rXua+bN87UoLroot3MN\nG+bjFebO9a6d9u09lLRtm/t1i0hlyqcOw7gQQrMQQvOEr6vi9rkphLB/CGGPEMKZIYR5CcfYFkK4\nIYTQOYSwVwjhghBClsvcSGP27W97JcMtW5JXM5wxw0ssR9UTK42ZtzJELQzRDIkePdK/LpXrrvOW\nhKuu8hByzDHeIjBkCDz0UN39H38c9tjDp1Pm4stf9taM44/3OhYvvJB6jISINE5qTJS8jRgBl15a\nvOONHg1//CPcdZd/Eh8zpu4++Qx4bGiiio/gLQw9e+Zfpvrkk70Y1O9/70EgcuWVMHNm3RkZf/+7\nT5XMtWWga1df/GrnTp+G+ZnP5He9IlK5FBgkL5s2eU2Dxx7zqYqFWrrU10k47zz42td80aYxY2rX\nGQihcQSG/v19HMC6ddmXhE6nXbu6284802c0xLcyzJnjC2Hl2h0RefRRmDy5dIt2iUjDpsAgeXnm\nGe8z33tvuPPOwo61e7cP4GvVykOImX8KXrTIqzpGli71AYKVHhiigY8zZuQ3pTIbLVp4RcjHHoPt\nsdFDf/+7D3QcPDi/Yx58sK/+KSJNkwKD5OWxx3xU/ne+4yWKC1nX4I47vGLgww97FUPw2gJ77lm7\nWyIa8FjpgaFXL5+p8eKLHoAKbWFI5YorvE7CP//prTN/+5sPkqyqKs35RKRxU2CQnK1cCWPHwiWX\nwLXX+rS6P/whv2Nt2wY//Sn89397RcFI69Y+WyIxMOy9N3TrVtj1l1uLFtC3b80shlK0MIAHkWOP\n9UJR77zjXRL5dkeIiCgwSM4ef9wfL7wQOnb0T7J33+31EXI1ebJPpbzssrrPnXOOL0kdtV5Mn+6t\nC5ZsjdQK07+/V1xs0cIrNpbKFVf4IMW77vKwFR/KRERyocAgOXvsMR9UF02r++Y3/U092bz/TMaN\n80F7yboZvvQlf3z+eX9sDAMeI9E4hkJmSGTj4ot9NckHHvCpkbmsvyEiEk+BQXLy4YdeHCh+OuVh\nh/m8/9tvz331xHHjfLpe8+Z1n+vSxVdCfPZZn/v/4YeNLzCUavxCpGNHn3kC6o4QkcIoMEhO/vpX\nH4w4ZEjt7d/6lo/4f/HF7I+1Ywf8+9/pF4M65xwfLzFlin/fWAJDnz7eslDqwAA+MHXIkOwWmhIR\nSUWBQbIWgndHnH9+7SJBAJ/7nM/Pv/327I83darXc8gUGDZsgP/7P29ObyzT+qqqfPbCddeV/lwD\nB3pRrBbFXptWRJoUBQbJ2tSpvpbAJZfUfc7MWxlefBEmZbmE2LhxXnGwf//U+/TrBwccAKNG+afx\nxtQHf/rpNdNIRUQaOgUGydpjj3mJ4FRN2xdd5F0G11/vxZgyGTcOBg1KP+jPzFsZoPF0R4iIVCIF\nBsnKzp1e+Ofii1M3bTdv7msaTJ4MDz6Y+XhvvJG+OyJy7rn+qMAgIlI+CgySlRdegGXLvNxwOoMG\neU2FH/wA1qxJvd+MGT42IZvAcOqpcMEFNS0NIiJS/xQYJCt/+pMPakw33iBy662+fsFPfpJ6n3Hj\nvDzyscdmPl5VlReL0gqJIiLlo8AgGS1Z4iP6/+u/stu/a1f4+c99qerE5ZUj48b5WhSNaRCjiEhj\npsDQgK1f7ys0ltuDD3prwPDh2b/m+uvhiCOSD4DctQvGj8+uO0JERBoGBYYGaMcOX5vhM5+B44/P\nvXpiMe3a5UtODx/uJZyz1bKl107497/99fHeeQfWrVNgEBGpJAoMDUgIXgb5s5+FG27wVQw//hjm\nzy/uedatg6efhi1bMu/70kuwaFH23RHxvvAF+OpX4etfr73OxLhxvhrlccflfkwRESkPBYYGYts2\nnz44ZIgXKpo2zYsVAUyYULzzrF7tdRTOPx8OPBC++930geRPf/LiSdkMTkzmnnt81sRXvgIPP+zb\nxo3zlpOqqvyOKSIi9U+BoYH47//2T/NPPQUvv+w1Bzp29FaGYgWGlSv9U//ixT6I8corvbugRw+f\nsjhjRu39P/kEnnnGWxfyXVI6Winx6qv9fPfdB6+/ru4IEZFKo8DQANx/v3+Sv+ceX4I4/s150KDi\nBIblyz0srFgBr73mS0f/9rc+A+K++7yVYeBAnxK5a5e/5qGHfBZDslLQuWjWzGdMXHutd1GsXq3A\nICJSaRQYyuytt3wBoq99zT+FJxo0yFeBXLs2/3N88gmccooXUnrtNW+1iOyxh593+nQYMQJ++EMP\nFiNcAjoAABFwSURBVAsWeJC46CLo0CH/c0eaNfOBnN/8pk+7PP74wo8pIiL1R4GhjJYvh2HDvCDS\n//5v8n0GDfLHiRPzO8fs2XDSSbBxo48dSLXaY+vW3rrwr3/BwoU+JXLBgvwGO6ZiBnfc4V0iiatd\niohIw9ZoAsPWrb5UciUIwcPChRd68/8TT/gbdjLdu8O+++bXLfHii14cqU0br3tw2GGZX3PyyfD2\n2z6N8qyz/PXF1rx58Y8pIiKllWIZocqya5eP/F+zxpdgbtu23FdU16hRvtrjvHn+tXGj1yp49VXY\nf//UrzPLbxzD73/vzf9nngkjR+ZWQ6F9e/jzn3M7n4iING6NooXhrrvgzTe9XsCIEeW+mrq2bPGp\nhe+/77UHfvYznw3x/vveXZDJoEEwaZIXdMpk1y6vrnjDDf71zDO5hQUREZFkKr6FYf58+PGP/c3x\ns5/1PvfBg322QUPx7LO+MuOTT/oUxlwNGuShY/p0n8mQSgg+gPL+++Hee31GgoiISDFUdAtDCB4Q\n9t0XfvlLH+1//vlwzTU+XTCf4z3+eH6vTecvf/GWhXzCAvgKkVVVmbslfvlLDwr33aewICIixVXR\ngeGBB3wMwJ/+BHvu6f39993ng/wuu6xm0aMdO+Af//CWh29+EzZvrnusrVu9GuFFF/l+xRpAuWoV\nPP88XHpp/sdo1corLaYLDA8+6MtJ33KLF0gSEREppooKDBMn1qx/sGQJfPvb/uZ4xhk1+3TsCI8+\n6tMDf/ITuPlmOOQQn5Gwdq2Hi6OPhsmTa16zYgWceqp3GfzP/3g3xzXXFGfRp3/8w49z4YWFHSca\n+Jjsmp57zlsUrr3Wu2dERESKraICw/XXeyAYPBguuMDn8v/ud3X3+8IXfI2EX/3Kawuccw7MnOkD\nI6dNg732ghNPhF/8wsshDxwIH37oRY2+/32fIfC3v3nNgEQ7dnjp5nXrsrvmxx7zQLPvvgX96Awa\nBMuWeW2EeJMm+b045xyfGZFvCWcREZF0KiowPP64v8nv3OlLJP/xj7D33sn3/cUvfEXGJUu8X79v\nX99++OHeUvGDH/hshaOP9kqGkybVrJ54wQXwve/517/+5dtCgDFjfGDl6af7eIS7704/c2HBAm8V\nKKQ7InLiif4Y3y0xaZJPmzzqKJ86qfoGIiJSKhUVGLp3926Il17yWQfnnZd635YtYejQ5GWNW7b0\nvv7x470U8htvQLdutff55S+9peLCC32hptNO89UkDzjAzz9kSM3MjDFjkncV/PWvXhMi3XVmq2NH\nr74YBYaJEz24HHmkj5Fo06bwc4iIiKRSUYGh2E480bst9tyz7nMtWni3xJ57enP/0qU+PfLllz08\nPPigd28ccIAHiaFDYf36mteH4LMjhg4tXiGpaBzD+PHezdGvH7zwguosiIhI6TXpwJBJp04wdiw8\n8oiXSz7nnNpjBI46ygPEU0/5+IdBg+Cjj/y56dNhzpzidEdEooWozjrLZ008/7yPxxARESk1BYYM\nevb06ZYtWyZ/3syLRL35pk/XHDjQWwEee8wHOp52WvGu5XOf85aLQYO8G6QhlsAWEZHGqeIrPTYU\nRxzhS1UPG+brWrRpA5df7l0bxdK9u08H7dPHCzmJiIjUF7UwFFHnzj4g8pJLfDzDZZcV/xzHHKOw\nICIi9U+BochatfIKlEuWwIAB5b4aERGR4lBgKAEz2G+/cl+FiIhI8SgwiIiISEYKDCIiIpKRAoOI\niIhkpMAgIiIiGSkwiIiISEYKDCIiIpKRAoOIiIhkpMAgIiIiGSkwiIiISEYKDCIiIpJRWQODmV1n\nZgvMbIuZvWlmx5bzepq6kSNHlvsSGjXd39LTPS4t3d/Sa8j3uGyBwcwuAn4H/Aw4GpgJjDWzzuW6\npqauIf+iNga6v6Wne1xaur+l15DvcTlbGEYA94YQHgkhzAGuBTYDV5XxmkRERCSJsgQGM2sJDABe\nibaFEALwMnBCOa5JREREUitXC0NnoDmwPGH7cqBr/V+OiIiIpNOi3BeQpSqA2bNnl/s6GrXq6mqm\nTZtW7stotHR/S0/3uLR0f0uvFPc47r2zqpDjmPcE1K9Yl8RmYFgI4Zm47Q8B7UMIX07Y/z+Bx+r1\nIkVERBqXS0IIf833xWVpYQgh7DCzqcCpwDMAZmax7+9K8pKxwCXAR8DWerpMERGRxqAKOAR/L81b\nWVoYAMzsQuAhfHbEJHzWxH8Ah4cQVpblokRERCSpso1hCCE8Hqu5cDPQBZgBnKmwICIi0vCUrYVB\nREREKofWkhAREZGMFBhEREQko4ICg5l9zsyeMbMlZrbbzIYkPL+vmT0Ue36TmT1nZj2SHOcEM3vF\nzDaaWbWZvWZmrRP2OTu2QNVmM1tjZk9lcX19zez12OJWC83suwnPdzWzx8xsrpntMrPb870XpVAf\n99fMTo4de1fsMf5rQIbrS3t/Y/tcZ2azYv/dZpvZVwq9L8VUX7/DZtbfzF40s7VmttLM7jWztllc\nX6bf4ei/X/zXLjPbt5D7UiyF3l8zOzjN7+ewuP1+ZGYTYsdYk8P1VfTfCKifexzb534zmx/7f/kD\nM7vJfIp8putr8n8nsv09jtu/lZnNiD3fN4vrq5e/E4W2MLTFByv+PyDZYIjR+FSOc4GjgEXAy2bW\nJtrBzE4AngdeAI6Jff0e2B23zzDgEeAB4LPAiUDauaRmthc+hWQB0B/4LnCTmV0Tt1trYAVwS+zn\naGjq4/5OwKtr7hd77ArcD8wPIUxNdWHZ3F8z+zrwS+CnwJHATcDdZnZ29reg5Ep+j81sP+Al4H1g\nIHAW0BufJZRSlr/DxK77MGr+++0XQliRxc9eHwq9v4uo+/v5M2ADfs8jLYHHgT9ke2GN5G8E1M89\nPhww4L/w/5dH4DPcfpnuwvR3Iuff48htwOIU56qlXv9OhBCK8oX/cRwS9/1hsW2Hx20zvPzzVXHb\nJgI3pTluc+Bj4Iocr+frwCqgRdy2XwOzUuz/L+D2Yt2PYn+V6v4mOU+L2DF+VOj9xcPIrQmv+y3w\nernvZ33eY/yP7CcJ2/rEjv2ZAu/xycAuoF2571+p7m+S40wD/pTiucuBNVleT6P6G1Ff9zhun+8A\n8wq9x03l70S29xgYDLyHh7TdQN8i3OOi/J0o5RiG1nii2RZtCH7l24CTAMxsH+A4YFWsOXGZeVPu\noLjj9Af2j+0/zcyWxpp7emc4//H4L9zOuG1jgV5m1r7QH64BKNb9TXQe0JEMn37J7v62pm6hra3A\nQDNrnuH4DUGx7nFrYHvCsaP7clKa82f7O2zAjNj/Gy+a2YnZ/4hllfH+JjLvJjsKb20sVGP/GwGl\nvccdgEzdP/o7kUSqe2xmXYA/AZcCW7I8f739nShlYJiDtwz82sw6xPpkvg8ciDfLAHwm9vgz4F7g\nTDx1vWJm3eP2sdg+NwNnA2uB18ysQ5rzdyX54lbRc5WuWPc30VXA2BDC0gznz+b+jgWuMbP+AGZ2\nDHA13nzcOcPxG4Ji3eNXga5m9h0za2lme+OfAELccZLJ5h5/AnwNGAacH7ve18zsqJx/2vqXzf1N\ndDX+yemtIpy/sf+NgBLd41j//PXAHzOcX38nkkt1j/8M3BNCmJ7D+evt70TJAkMs7XwZ6Imn0I14\ns8hz1PSfR+f/YwjhkRDCzBDCt4C5+BtX/D6/CCGMit3IK/E/thcAmNm7ZrYh9vXPUv1MDUkR7++n\nzOwA/A3v/oTt+d7fW/D+uYlmtgN4mpqWi92pXtRQFOsehxBm4U3l38LXUFkKzMf7xqNxDnnd4xDC\n+yGE+0II00MIb4YQrgb+jfcxN2hZ3t9PmVkVMJyE389sNMW/EVCaexz7O/E88PcQwoNx2/V3ooB7\nbGb/DewJ3BptSvLasv6dKGmlx9ibe//YoIxWIYTVZvYmMDm2yyexx8RlKGcD3VLtE0LYbmbz4/YZ\njKdRqGnGWYZXkIzXJe65ilek+xvvKrwv7NmE7Xnd3xDCVvyTw9diz0Upd0OokIqexbrHIYS/AX+L\ndWFsim3+Nh4coLi/w5OAdN1ODUYW9zfeBUAb4NE8TtUk/0ZAce+xme2Pt5i9EUL4WsLT+jtR2D3+\nAnACsM2sVlaYYmaPhRCupMx/J+qlDkMIYUPsBh6GjyAfFdv+Ef5pq1fCS3oCC2P/nor3BX26j/lU\nnkOifUIIH4cQ5se+oj/gE4HPJ/SBnQHMDSFUF/HHK7sC72+8K4CHQwi7Eo5f0P0NIewKISyN9etd\nTN1A0uAV6x6HEFaGEDbj92ELPnui2L/DR1ETZCpCqvub4CrgmRDC6jyO36T/RkDh9zjWsvAv/E2w\nTgul/k4UfI9vAPrFfQ3GW9IvBH4cO355/06EwkaLto39YEfhTS/fjH1/UOz5/8CbZg7FB9MtAB5P\nOMY38DEJw4DuePPUJuDQuH3uwKelnI7/Ib4/9oO2T3Nt7fA/5A/jU3UuwpuKrk7YL7r+yXji6wcc\nUch9KdZXfd3f2H6n4qNoe2Z5bRnvLz56+BKgBz6d8G/ASqBbue9tGX6HrwOOjt2T62LPX1eEe/wN\nYEjsvL2BO4EdwCnlvrfFur+x/XrEfj9PT3Geg2LH/SlQTc0f3baF3N/Yfg32b0R93WN84PkHwIux\nf3eJvorwO9wk/k5k83ucsO/BZDdLot7+ThR6E0+O/UC7Er4ejD1/A/5GvzV2A28ibupH3HG+h38a\n2wC8AZyQ8HxzfF7qJ8A6fJBMxv9h8alr4/B+40XAd5Lsk+z655f7F7Q+729sn8fIcRpTpvuLTwua\nFvvlXQs8BRxW7vtapt/hh/E/gluA6cB/Fukefxf/Q74pdvxXgM+X+76W4P7+EliQ5jx/TnKOXZnu\nRaX/jaive4yPwUk8/m5gVxF+h5vS34m0v8cJ+x4cO0fawJDlPS7K3wktPiUiIiIZaS0JERERyUiB\nQURERDJSYBAREZGMFBhEREQkIwUGERERyUiBQURERDJSYBAREZGMFBhEREQkIwUGERERyUiBQUTK\nxsx2m9mQcl+HiGSmwCDSBJnZQ7E363uSPHd37LkHi3i+n5nZ9GIdT0TqnwKDSNMU8EVqLjaz1tHG\n2L+Hk3z582KcU/5/e/cPWlcZh3H8+2AIKIouWhxERAQdJEi1Qy0IDt06mF3FFEG6FUqRDLo4CKFL\nKXRrITRQBTtp0SwO/hlswUGEUqoILZT+oaK2jSaxvw7nJD1cLz0hbZpKvp/lnvu+533vOcO99zm/\n+x6u9D9lYJA2rh+Bs8B4p22cJiwsVwOSjCbZn+RCkrkk3yR5udP/WluReD3JiSTXknyX5Lm2/23g\nQ2Cs3e/fJG91XvPxJMfacaeT7FjLk5a0OgYGaeMq4BAw0WmboPmr6HTapoA3gDeBl4AzwFdJHhuY\n7yNgN7AZWGznBvgE2Af8DGwCnmzblnwAHAVeBI4DM0PmlrTODAzSxjYDbEvyVJKnga3AkaXOJA8B\n7wF7qmq2qk4B7wJzwM7OPAVMVtW37T4fA1uTjFbV38BVYLGqLlXVxar6pzP2cFV9WlW/ApPAw8CW\ntTtlSasxst4HIGn9VNXlJJ8D79BUFb6oqivJcoHhWZrPie87YxaT/AC8MDDdT53t8+3jE8C5nsNY\nHldV15P82Y6TdB8xMEg6DBygqRLsGtKfIW3DLHS2lxY4rqSKuTDwvFY4TtI95JtS0pfAKM0FxOxA\n3y/APPDqUkOSEeAVmjUJKzUPPHBnhylpPVlhkDa4qrqR5Pl2uwb6ric5CEwl+Z3mroq9wIPcWtQI\nw6sQ3bbfgGeSjNH8RPFXVc3fvbOQtNYMDJKoqqu36X6f5st/GngEOAlsr6o/ulMMm7az/RnNnRZf\nA4/SrJmYXsE4SfeJDFxQSJIk/YdrGCRJUi8DgyRJ6mVgkCRJvQwMkiSpl4FBkiT1MjBIkqReBgZJ\nktTLwCBJknoZGCRJUi8DgyRJ6mVgkCRJvW4CIvDx3uTuqMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbecdf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "series.plot()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAFkCAYAAADL+IqjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzXQOdfYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc038e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(1)\n",
    "pyplot.subplot(211)\n",
    "series.hist()\n",
    "pyplot.subplot(212)\n",
    "series.plot(kind='kde')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas import Series\n",
    "from pandas import DataFrame\n",
    "from pandas import TimeGrouper\n",
    "from matplotlib import pyplot\n",
    "groups = series['1966':'1973'].groupby(series.index[:84].map(lambda t:t[:4]))\n",
    "years = DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1966     482.0\n",
       "1967     672.0\n",
       "1968    1178.0\n",
       "1969    1352.0\n",
       "1970    1753.0\n",
       "1971    2503.0\n",
       "1972    3285.0\n",
       "Name: Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977), dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-126-26777540950d>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-126-26777540950d>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    pyplot.show\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1966-01     41.0\n",
       "1966-02     39.0\n",
       "1966-03     50.0\n",
       "1966-04     40.0\n",
       "1966-05     43.0\n",
       "1966-06     38.0\n",
       "1966-07     44.0\n",
       "1966-08     35.0\n",
       "1966-09     39.0\n",
       "1966-10     35.0\n",
       "1966-11     29.0\n",
       "1966-12     49.0\n",
       "1967-01     50.0\n",
       "1967-02     59.0\n",
       "1967-03     63.0\n",
       "1967-04     32.0\n",
       "1967-05     39.0\n",
       "1967-06     47.0\n",
       "1967-07     53.0\n",
       "1967-08     60.0\n",
       "1967-09     57.0\n",
       "1967-10     52.0\n",
       "1967-11     70.0\n",
       "1967-12     90.0\n",
       "1968-01     74.0\n",
       "1968-02     62.0\n",
       "1968-03     55.0\n",
       "1968-04     84.0\n",
       "1968-05     94.0\n",
       "1968-06     70.0\n",
       "           ...  \n",
       "1972-05    212.0\n",
       "1972-06    246.0\n",
       "1972-07    353.0\n",
       "1972-08    339.0\n",
       "1972-09    308.0\n",
       "1972-10    247.0\n",
       "1972-11    257.0\n",
       "1972-12    322.0\n",
       "1973-01    298.0\n",
       "1973-02    273.0\n",
       "1973-03    312.0\n",
       "1973-04    249.0\n",
       "1973-05    286.0\n",
       "1973-06    279.0\n",
       "1973-07    309.0\n",
       "1973-08    401.0\n",
       "1973-09    309.0\n",
       "1973-10    328.0\n",
       "1973-11    353.0\n",
       "1973-12    354.0\n",
       "1974-01    327.0\n",
       "1974-02    324.0\n",
       "1974-03    285.0\n",
       "1974-04    243.0\n",
       "1974-05    241.0\n",
       "1974-06    287.0\n",
       "1974-07    355.0\n",
       "1974-08    460.0\n",
       "1974-09    364.0\n",
       "1974-10    487.0\n",
       "Name: Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977), dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "years = DataFrame(series['1966':'1973'].values,index=[series.index[:84].map(lambda t:t[:4]),series.index[:84].map(lambda t:t[5:])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "years=years.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">0</th>\n",
       "      <th>01</th>\n",
       "      <td>41.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>39.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>50.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>40.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>43.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>38.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>44.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>39.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>49.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1966  1967   1968   1969   1970   1971   1972\n",
       "0 01  41.0  50.0   74.0  158.0   99.0  136.0  238.0\n",
       "  02  39.0  59.0   62.0  124.0  107.0  161.0  213.0\n",
       "  03  50.0  63.0   55.0  140.0  112.0  171.0  257.0\n",
       "  04  40.0  32.0   84.0  109.0   90.0  149.0  293.0\n",
       "  05  43.0  39.0   94.0  114.0   98.0  184.0  212.0\n",
       "  06  38.0  47.0   70.0   77.0  125.0  155.0  246.0\n",
       "  07  44.0  53.0  108.0  120.0  155.0  276.0  353.0\n",
       "  08  35.0  60.0  139.0  133.0  190.0  224.0  339.0\n",
       "  09  39.0  57.0  120.0  110.0  236.0  213.0  308.0\n",
       "  10  35.0  52.0   97.0   92.0  189.0  279.0  247.0\n",
       "  11  29.0  70.0  126.0   97.0  174.0  268.0  257.0\n",
       "  12  49.0  90.0  149.0   78.0  178.0  287.0  322.0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\memedunno\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAFkCAYAAACw3EhvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIoH/H39i5RpKQebhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4602e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years.T.boxplot()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -3.980946\n",
      "p-value: 0.001514\n",
      "Critical Values:\n",
      "\t5%: -2.893\n",
      "\t1%: -3.503\n",
      "\t10%: -2.584\n"
     ]
    }
   ],
   "source": [
    "from pandas import Series\n",
    "from statsmodels.tsa.stattools import adfuller \n",
    "def difference(dataset):\n",
    "    diff = list()\n",
    "    for i in range(1,len(dataset)):\n",
    "        value = dataset[i] - dataset[i-1]\n",
    "        diff.append(value)\n",
    "    return Series(diff)\n",
    "X = series.values\n",
    "stationary = difference(X)\n",
    "stationary.index = series.index[1:]\n",
    "result = adfuller(stationary)\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])\n",
    "print('Critical Values:')\n",
    "for key, value in result[4].items():\n",
    "    print('\\t%s: %.3f' % (key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1966-01     41.0\n",
       "1966-02     39.0\n",
       "1966-03     50.0\n",
       "1966-04     40.0\n",
       "1966-05     43.0\n",
       "1966-06     38.0\n",
       "1966-07     44.0\n",
       "1966-08     35.0\n",
       "1966-09     39.0\n",
       "1966-10     35.0\n",
       "1966-11     29.0\n",
       "1966-12     49.0\n",
       "1967-01     50.0\n",
       "1967-02     59.0\n",
       "1967-03     63.0\n",
       "1967-04     32.0\n",
       "1967-05     39.0\n",
       "1967-06     47.0\n",
       "1967-07     53.0\n",
       "1967-08     60.0\n",
       "1967-09     57.0\n",
       "1967-10     52.0\n",
       "1967-11     70.0\n",
       "1967-12     90.0\n",
       "1968-01     74.0\n",
       "1968-02     62.0\n",
       "1968-03     55.0\n",
       "1968-04     84.0\n",
       "1968-05     94.0\n",
       "1968-06     70.0\n",
       "           ...  \n",
       "1972-05    212.0\n",
       "1972-06    246.0\n",
       "1972-07    353.0\n",
       "1972-08    339.0\n",
       "1972-09    308.0\n",
       "1972-10    247.0\n",
       "1972-11    257.0\n",
       "1972-12    322.0\n",
       "1973-01    298.0\n",
       "1973-02    273.0\n",
       "1973-03    312.0\n",
       "1973-04    249.0\n",
       "1973-05    286.0\n",
       "1973-06    279.0\n",
       "1973-07    309.0\n",
       "1973-08    401.0\n",
       "1973-09    309.0\n",
       "1973-10    328.0\n",
       "1973-11    353.0\n",
       "1973-12    354.0\n",
       "1974-01    327.0\n",
       "1974-02    324.0\n",
       "1974-03    285.0\n",
       "1974-04    243.0\n",
       "1974-05    241.0\n",
       "1974-06    287.0\n",
       "1974-07    355.0\n",
       "1974-08    460.0\n",
       "1974-09    364.0\n",
       "1974-10    487.0\n",
       "Name: Monthly Boston armed robberies Jan.1966-Oct.1975 Deutsch and Alt (1977), dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'function' object has no attribute 'figure'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-167-5ca0bb4b0128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m211\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mplot_acf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m212\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplot_pacf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\memedunno\\Anaconda2\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py\u001b[0m in \u001b[0;36mplot_acf\u001b[0;34m(x, ax, lags, alpha, use_vlines, unbiased, fft, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \"\"\"\n\u001b[0;32m---> 61\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_mpl_ax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlags\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\memedunno\\Anaconda2\\lib\\site-packages\\statsmodels\\graphics\\utils.py\u001b[0m in \u001b[0;36mcreate_mpl_ax\u001b[0;34m(ax)\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m111\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'figure'"
     ]
    }
   ],
   "source": [
    "from pandas import Series\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from matplotlib import pyplot\n",
    "pyplot.figure\n",
    "pyplot.subplot(211)\n",
    "plot_acf(series, ax=pyplot.gca)\n",
    "pyplot.subplot(212)\n",
    "plot_pacf(series, ax=pyplot.gca)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
